A novel vine copula-based dependence description (VCDD) process monitoring approach is proposed. The main contribution is to extract the complex dependence among process variables rather than perform dimensionality reduction or other decoupling processes. For a multimode chemical process, the C-vine copula model of each mode is initially created, in which a multivariate optimization problem is simplified as coping with a series of bivariate copulas listed in a sparse matrix. To measure the distance of the process data from each non-Gaussian mode, a generalized local probability (GLP) index is defined. Consequently, the generalized Bayesian inference-based probability (GBIP) index under a given control limit can be further calculated in real time via searching the density quantile table created offline. The validity and effectiveness of the proposed approach are illustrated using a numerical example and the Tennessee Eastman benchmark process. The results show that the proposed VCDD approach achieves good performance in both monitoring results and computation load.

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This study proposes an online monitoring technique for nonlinear multiple-mode problems in industrial processes. The contributions of the proposed technique are summarized as follows: 1) Lazy learning (LL), a new adaptive local modeling method, is introduced for multiple-mode process monitoring. In this method, multiple modes are separated and accurately modeled online, and the between-mode dynamic process is considered.2) The modified receptor density algorithm (MRDA) exhibiting superior nonlinear ability is introduced to analyze the residuals between the actual system output and the model-predicted output. The simulation of the Tennessee Eastman process with multiple operation modes shows that compared with other techniques mentioned in this study, the proposed technique performs more accurately and is more suitable for nonlinear processes with multiple operation modes.Note to Practitioners-This paper was motivated by the problem of fault detection of chemical process. Most chemical processes are nonlinear processes. Especially in the multiple-mode process, nonlinear is more significant, making the process more difficult to monitor. In this paper, we modify the receptor density algorithm (MRDA) and combine it with support vector data description (SVDD) and LL algorithm to monitor nonlinear multiple operation modes chemical processes. In LL-SVDD-MRDA, LL is introduced to solve multiple operation modes, SVDD is adopted to construct one-dimensional distance statistics to reflect the change in high-dimensional data and MRDA is used for nonlinear data monitoring. Our simulation demonstrates that LL-SVDD-MRDA is a highly competitive method for handling nonlinear multiple operation modes chemical processes monitoring.Index Terms-Between-mode dynamic process, lazy learning (LL), modified receptor density algorithm (MRDA), multiple operation modes, nonlinear, support vector data description (SVDD). 1545-5955